Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Nemec, Dušan; Andel, Jan; Šimák, Vojtech; Hrbček, Jozef

doi:10.3390/s23146431

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…In addition, Dusan et al proposed an adaptive sensor weighting adjustment method based on the root mean square error (RMSE) of the weighted average value of all sensors. This method suppresses degraded sensors while maintaining the overall estimation accuracy [28]. Li et al also developed a comprehensive framework that combines adaptive dead reckoning (ADR) with zero-velocity update (ZUPT).…”

Section: Introductionmentioning

confidence: 99%

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Cao,

Luo,

Liu

et al. 2024

Sensors

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In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

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Section: Introductionmentioning

confidence: 99%

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Cao,

Luo,

Liu

et al. 2024

Sensors

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“…This algorithm meets the needs of online implementation by supporting multi-resolution terrain access, thereby generating high-precision real-time paths within the allotted timeframe. The authors of [34] proposed a uniform sensor fusion optimization method, which can not only estimate the calibration constant of sensors in real time but also automatically suppresses degraded sensors while maintaining the overall accuracy of the estimation. The weights of the sensors are adaptively adjusted based on RMSE, involving the weighted average of all sensors.…”

Section: Introductionmentioning

confidence: 99%

Research on the Model of a Navigation and Positioning Algorithm for Agricultural Machinery Based on the IABC-BP Network

Yue,

Shang,

Feng

et al. 2023

Agriculture

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Improving the positioning accuracy and stability of a single BDS/INS sensor system in agricultural machinery is important for expanding the application scenarios of agricultural machinery. This paper proposes a navigation and positioning model based on an improved bee-colony-algorithm-optimized BP network (the IABC-BP model). The main aspect of this work involves introducing adaptive coefficients and speed adjustment coefficients that obey Gaussian distribution to ensure the balance between the rate of convergence, group flexibility, and searchability in the search process. The implicit adaptive layer formula of the BP network is proposed, and the BDS/INS navigation and positioning model for agricultural machinery is established using the IABC algorithm and the Kalman filter. Simulation tests and analyses of real-world application scenarios were conducted on the model, and the results showed that, compared with the original model, the performance of the model improved by 90.65%, 84.11%, and 25.96%, indicating that the proposed model has high accuracy and effectiveness. In the information fusion and compensation correction mode, the algorithm processes errors such as longitude and latitude within the target range and can achieve reliable navigation and positioning accuracy in a short period. At the same time, the model has good stability and generalization ability, and can be applied to other navigation scenarios in the future to expand its application scope.

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